{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning models from data: Mauna Loa CO$_2$ record\n",
    "\n",
    "## Motivation: Climate Change\n",
    "\n",
    "> _The climate emergency is one of the most pressing issues of our time. At this pivotal moment, the decisions and actions we take today will reverberate beyond our own borders and lifetimes._ -- [UBC Climate Emergency Engagement](https://climateemergency.ubc.ca/)\n",
    "\n",
    "Carbon Dioxide (CO$_2$) is a greenhouse gas, and significant contributor to the warming of the climate. It is an important input into climate models, which we use to make predictions about possible future climate scenarios, and sometimes to make policy decisions, including carbon taxes. \n",
    "\n",
    "**Question:** Based on historical CO$_2$ data, can we predict what CO$_2$ concentrations will be in the future? \n",
    "\n",
    "## Learning Goals\n",
    "- explore the idea of \"learning\": building a model from data that lets us make predictions \n",
    "- introduce the principles of linear regression, a simple, but powerful method for estimating a linear model of data\n",
    "- explore the impact of outliers in data on the model \n",
    "- discuss the limitations of models\n",
    "\n",
    "## Context\n",
    "\n",
    "Scripps institute of Oceanography has a research station at Mauna Loa in Hawaii where they have been measuring atmospheric CO$_2$ since 1958. The data we will focus on are the seasonally adjusted data. \n",
    "\n",
    "<img src=\"https://scrippsco2.ucsd.edu/assets/images/mlo_station_map.png\" align=\"center\">\n",
    "\n",
    "**Data Source**\n",
    "\n",
    "C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and  H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and  oceans from 1978 to 2000.  I. Global aspects, SIO Reference Series, No. 01-06, Scripps  Institution of Oceanography, San Diego, 88 pages, 2001. https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html   \n",
    "\n",
    "## Running the notebook\n",
    "- Each block of code can be run with `shift` + `enter` or from the menu with `Cell` + `Run Cells`\n",
    "- If you need to restart the notebook, go to the menu: `Kernel` + `Restart`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://bit.ly/eds-2020-demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the python packages we use in this lesson \n",
    "\n",
    "These are standard packages in python, \n",
    "- `numpy` is the numerical array package in python\n",
    "- `scipy.stats` is what we will use to estimate a linear model \n",
    "- `matplotlib` has tools for plotting\n",
    "- `ipywidgets` lets us connect interactive components like slide bars and toggle buttons to the code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import ipywidgets as widgets\n",
    "\n",
    "# set a larger font size for viewing \n",
    "from matplotlib import rcParams\n",
    "rcParams[\"font.size\"] = 14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "co2_data = np.load(\"mauna_loa_data.npz\")\n",
    "dates = co2_data[\"dates\"]\n",
    "co2 = co2_data[\"co2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first year in the data is 1958 and the last year in the data set is 2020.\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"The first year in the data is {int(dates.min())} \"\n",
    "    f\"and the last year in the data set is {int(dates.max())}.\" \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define some helper functions for fetching data\n",
    "\n",
    "- `get_data_between`  : fetches the seasonally adjusted CO$_2$ data from Mauna Loa between `year_min` and `year_max`\n",
    "\n",
    "- `plot_co2_data` plots the data between `year_min` and `year_max`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data_between(year_min=1958, year_max=2020, data_type=\"seasonally adjusted\"):\n",
    "    \"\"\"\n",
    "    A function to fetch data between year_min and year_max  \n",
    "    \"\"\"\n",
    "        \n",
    "    # find the data between the minimimum and maximum years\n",
    "    indices = (dates >= year_min) & (dates <= year_max) \n",
    "    \n",
    "    return dates[indices], co2[indices]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_co2_data(dates, data, ax=None):\n",
    "    \"\"\"\n",
    "    A function that we can use to plot data between year_min and year_max\n",
    "    \"\"\"\n",
    "    \n",
    "    # create a figure if one isn't supplied\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "        \n",
    "    # plot data\n",
    "    ax.plot(dates, data, '.',  ms=8)\n",
    "    ax.grid()\n",
    "    ax.set_xlabel(f\"Year\")\n",
    "    ax.set_ylabel(f\"Seasonally adjusted CO$_2$ [ppm]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the whole data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_co2_data(dates, co2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Focus on the first 5 years\n",
    "\n",
    "**Question:** If the trend between 1958 and 1963 continues, what would we expect the CO$_2$ concentration to be in January, 2030?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "early_year = 1963\n",
    "dates_before_early_year, co2_before_early_year = get_data_between(year_max=early_year)\n",
    "plot_co2_data(dates_before_early_year, co2_before_early_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: The \"eyeball\" norm \n",
    "\n",
    "- using the widgets below, estimate a slope and intercept of a line that fits the data\n",
    "- the `slope` indicates the rate of CO$_2$ accumulation per year\n",
    "- the `intercept` is the estimated concentration of CO$_2$ at the first point we consider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_line(dates, slope, intercept, ax=None, label=None):\n",
    "    \"\"\"\n",
    "    A function to add a line to a plot\n",
    "    \"\"\"\n",
    "    \n",
    "    # create a figure if one isn't supplied\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "    \n",
    "    y = slope * (dates - dates.min()) + intercept\n",
    "    ax.plot(dates, y, label=label)\n",
    "\n",
    "def widget_fit_co2_data(slope, intercept, year_min=1958, year_max=2020):\n",
    "    \"\"\"\n",
    "    This function creates an interactive widget where we can fit a curve to data\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "    dates_in_range, data_in_range = get_data_between(\n",
    "        year_min=year_min, year_max=year_max\n",
    "    )\n",
    "    plot_co2_data(dates_in_range, data_in_range, ax=ax)\n",
    "    ax.set_ylim([data_in_range.min()-1, data_in_range.max()+1])\n",
    "    add_line(dates_in_range, slope, intercept, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "91aeae2f651848edbc331b30aaa538b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=2.0, description='slope', max=5.0), FloatSlider(value=313.44, descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_early_year = widgets.interactive(\n",
    "    widget_fit_co2_data, \n",
    "    slope=widgets.FloatSlider(min=0, max=5, step=0.1, value=2),\n",
    "    intercept=widgets.FloatSlider(min=co2_before_early_year.min()-1, max=co2_before_early_year.max()+1, step=0.25),\n",
    "    year_min=widgets.fixed(1958),\n",
    "    year_max=widgets.fixed(early_year),\n",
    ")\n",
    "w_early_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss with your neighbour\n",
    "\n",
    "- What are the features of a \"good fit\"? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a prediction \n",
    "\n",
    "Based on your estimated slope and intercept values what will the CO$_2$ concentration be in January 2030"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_year = 2030"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_co2(slope, intercept, initial_date, prediction_date):\n",
    "    \"\"\"\n",
    "    based on an estimated slope, and intercept use a linear \n",
    "    model to predict CO2 concentration\n",
    "    \"\"\"\n",
    "    return slope * (prediction_date-initial_date) + intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following lines of code grab the value of the slope and intercept from the widget above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "eyeball_slope_early = w_early_year.children[0].value\n",
    "eyeball_intercept_early = w_early_year.children[1].value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we use the values of the slope and intercept you estimated to predict the CO$_2$ concentration in the future. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted CO2 in 2030: 457.03 [ppm]\n"
     ]
    }
   ],
   "source": [
    "prediction_eyeball_early = predict_co2(\n",
    "    eyeball_slope_early,\n",
    "    eyeball_intercept_early,\n",
    "    dates_before_early_year.min(),\n",
    "    predict_year\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"Predicted CO2 in {predict_year}: {prediction_eyeball_early:1.2f} [ppm]\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2: Using least squares\n",
    "\n",
    "In a linear regression (a least-squares problem with one dimension), the measure of a `good fit` is \n",
    "\n",
    "$$\n",
    "\\phi = \\sum_i (d_i^{pred} - d_i^{obs})^2\n",
    "$$\n",
    "\n",
    "or if we treat form the vector $\\mathbf{d} = [d_0, d_1, ... d_n]$, then this is equivalent to the $l_2$ norm of the difference between the observed data and our prediction. \n",
    "\n",
    "$$\n",
    "\\phi = \\| \\mathbf{d}^{pred} - \\mathbf{d}^{obs} \\|^2\n",
    "$$\n",
    "\n",
    "So what we are asking for is to minimize the vertical distance between each point and the line. \n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Linear_least_squares_example2.svg/1920px-Linear_least_squares_example2.svg.png\" width=40%>\n",
    "\n",
    "\n",
    "We will walk through the derivation of the least-squares solution in the next lecture. For now, we will explore how to apply it to these data. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope from linear regression is:         0.82 ppm/year \n",
      "and the intercept from linear regression is: 314.96 ppm.\n"
     ]
    }
   ],
   "source": [
    "slope_early, intercept_early, _, _, _ = stats.linregress(\n",
    "    dates_before_early_year - dates_before_early_year.min(), co2_before_early_year\n",
    ")\n",
    "print(\n",
    "    f\"The slope from linear regression is:         {slope_early:1.2f} ppm/year \\n\"\n",
    "    f\"and the intercept from linear regression is: {intercept_early:1.2f} ppm.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### compare the least squares fit with your fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1246a0358>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "plot_co2_data(dates_before_early_year, co2_before_early_year, ax=ax)\n",
    "add_line(\n",
    "    dates_before_early_year, slope_early, intercept_early, ax=ax, \n",
    "    label=f\"regression fit\"\n",
    ")\n",
    "add_line(\n",
    "    dates_before_early_year, eyeball_slope_early, eyeball_intercept_early, ax=ax, \n",
    "    label=f\"eyeball norm\"\n",
    ")\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a prediction with the slope from linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted CO2 in 2030: 373.77 [ppm]\n"
     ]
    }
   ],
   "source": [
    "predict_year = 2030\n",
    "\n",
    "prediction_fit_early = predict_co2(\n",
    "    slope_early,\n",
    "    intercept_early,\n",
    "    dates_before_early_year.min(),\n",
    "    predict_year\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"Predicted CO2 in {predict_year}: {prediction_fit_early:1.2f} [ppm]\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Focus on recent data: the last 5 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "recent_year = 2015\n",
    "dates_after_recent_year, co2_after_recent_year = get_data_between(year_min=recent_year)\n",
    "plot_co2_data(dates_after_recent_year, co2_after_recent_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "15bd336abd9e4bffbfa8110cea1c097a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=2.0, description='slope', max=5.0), FloatSlider(value=398.56, descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_recent = widgets.interactive(\n",
    "    widget_fit_co2_data, \n",
    "    slope=widgets.FloatSlider(min=0, max=5, step=0.1, value=2),\n",
    "    intercept=widgets.FloatSlider(min=co2_after_recent_year.min()-1, max=co2_after_recent_year.max()+1, step=0.25),\n",
    "    year_min=widgets.fixed(recent_year),\n",
    "    year_max=widgets.fixed(2020),\n",
    ")\n",
    "w_recent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope from linear regression is:         2.56 ppm/year \n",
      "and the intercept from linear regression is: 400.08 ppm\n"
     ]
    }
   ],
   "source": [
    "slope_recent, intercept_recent, _, _, _ = stats.linregress(\n",
    "    dates_after_recent_year  - dates_after_recent_year.min(), co2_after_recent_year \n",
    ")\n",
    "print(\n",
    "    f\"The slope from linear regression is:         {slope_recent:1.2f} ppm/year \\n\"\n",
    "    f\"and the intercept from linear regression is: {intercept_recent:1.2f} ppm\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "eyeball_slope_recent = w_recent.children[0].value\n",
    "eyeball_intercept_recent = w_recent.children[1].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1107207b8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "plot_co2_data(dates_after_recent_year, co2_after_recent_year, ax=ax)\n",
    "add_line(\n",
    "    dates_after_recent_year, slope_recent, intercept_recent, ax=ax, \n",
    "    label=f\"regression fit\"\n",
    ")\n",
    "add_line(\n",
    "    dates_after_recent_year, eyeball_slope_recent, eyeball_intercept_recent, ax=ax, \n",
    "    label=f\"eyeball norm\"\n",
    ")\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict concentration using data from 2015 -- 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted CO2 in 2030: 438.31 ppm\n"
     ]
    }
   ],
   "source": [
    "predict_year = 2030\n",
    "\n",
    "prediction_fit_recent = predict_co2(\n",
    "    slope_recent,\n",
    "    intercept_recent,\n",
    "    dates_after_recent_year.min(),\n",
    "    predict_year\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"Predicted CO2 in {predict_year}: {prediction_fit_recent:1.2f} ppm\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare these models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted CO2 in 2030 using data from: \n",
      "  1958 - 1963: 373.77 ppm, with a slope of 0.82 ppm/year \n",
      "  2015 - 2020: 438.31 ppm, with a slope of 2.56 ppm/year\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    f\"Predicted CO2 in {predict_year} using data from: \\n\"\n",
    "    f\"  1958 - {early_year}: {prediction_fit_early:1.2f} ppm, with a slope of {slope_early:1.2f} ppm/year \\n\"\n",
    "    f\"  {recent_year} - 2020: {prediction_fit_recent:1.2f} ppm, with a slope of {slope_recent:1.2f} ppm/year\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## before looking at the next plot(!)\n",
    "\n",
    "Discuss with your neighbour - why are these so different? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(xlim=None, ylim=None):\n",
    "    dates_to_year_predicted = np.hstack([dates, np.r_[predict_year]])\n",
    "\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "    plot_co2_data(dates, co2, ax=ax)\n",
    "\n",
    "    # add predictions to 2030\n",
    "    add_line(\n",
    "        dates_to_year_predicted, slope_early, intercept_early, \n",
    "        ax=ax, label=f\"1958 - {early_year} prediction\"\n",
    "    )\n",
    "\n",
    "    add_line(\n",
    "        dates_to_year_predicted, slope_recent, \n",
    "        intercept_recent-((dates_after_recent_year.min() - dates.min())*slope_recent), # adjust the intercept to use 1958 as the \"zero\"\n",
    "        ax=ax, label=f\"{recent_year} - 2020 prediction\"\n",
    "    )\n",
    "    \n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.legend()\n",
    "\n",
    "plot_predictions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion:\n",
    "\n",
    "**Q1:** Within small enough regions, the data follow an approximately linear trend, so a linear model has some predictive power. In the following cell, we have a widget where you can zoom in on the plot. \n",
    "- Out to which year would you trust the model built with the data from 1958 - 1963? \n",
    "- Where does it start to break down?\n",
    "\n",
    "**Q2:** How far out would you trust our predictions with data from 2015 - 2020? Would you trust our model to predict CO$_2$ in the year 2050? \n",
    "\n",
    "**Q3:** How would you approach building a model to fit all of our data? \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8d84a912b5c54010bc4304a072a5f30a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntRangeSlider(value=(1958, 2031), continuous_update=False, description='xlim', max=2031…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widgets.interactive(\n",
    "    plot_predictions, \n",
    "    xlim=widgets.IntRangeSlider(min=dates.min(), max=predict_year+1, value=[int(dates.min()), predict_year+1], continuous_update=False),\n",
    "    ylim=widgets.IntRangeSlider(min=300, max=420, value=[300, 420],  continuous_update=False),\n",
    "   \n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore further: \n",
    "\n",
    "Try expanding the number of years included in each analysis. How many years does it take before a linear model is no longer appropriate?  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Upcoming topics:\n",
    "\n",
    "- Assessing quality of fit and the impact of outliers \n",
    "- Deriving the solution to the least squares problem\n",
    "- How do we incorporate data uncertainties into our model?\n",
    "- How do we fit models which are not linear? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## For next lecture - compare your fit to a least squares fit\n",
    "What impact does an outlier have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(2020)  # set a random seed so we are working with the same data\n",
    "\n",
    "slope = 1\n",
    "intercept = 0\n",
    "n_samples = 10\n",
    "noise_level = 0.1\n",
    "\n",
    "x = np.linspace(0, 1, n_samples)\n",
    "y = slope*x + intercept + noise_level * np.random.randn(n_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_outlier(outlier=n_samples-1, offset=0):\n",
    "    yy = y.copy()\n",
    "    yy[int(outlier)] = yy[int(outlier)] + offset\n",
    "    return yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data_with_outlier(outlier=n_samples-2, offset=0, ax=None):\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "    \n",
    "    yy = create_outlier(outlier, offset)\n",
    "    ax.plot(x, yy, 'o')\n",
    "    ax.grid(which=\"both\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_eyeball_norm_outlier(\n",
    "    outlier=n_samples/2, offset=0., slope=1, intercept=0\n",
    "):\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "        \n",
    "    plot_data_with_outlier(outlier, offset, ax)\n",
    "    add_line(x, slope, intercept, ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dbf56e6c89fd4677a6c974851bf22cce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=8, description='outlier', max=10), FloatSlider(value=1.0, description='o…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w_outlier = widgets.interactive(\n",
    "    plot_eyeball_norm_outlier, \n",
    "    outlier=widgets.IntSlider(min=0, max=n_samples, value=n_samples-2),\n",
    "    offset=widgets.FloatSlider(min=-5, max=5, value=1),\n",
    "    slope=widgets.FloatSlider(min=0, max=2, value=0.5),\n",
    "    intercept=widgets.FloatSlider(min=-1, max=1, value=0),\n",
    "    \n",
    ")\n",
    "\n",
    "w_outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "widget_outlier = w_outlier.children[0].value\n",
    "widget_offset = w_outlier.children[1].value\n",
    "widget_slope = w_outlier.children[2].value\n",
    "widget_intercept = w_outlier.children[3].value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope from linear regression is:         1.49 \n",
      "and the intercept from linear regression is: -0.20\n"
     ]
    }
   ],
   "source": [
    "slope_outlier_fit, intercept_outlier_fit, _, _, _ = stats.linregress(\n",
    "    x, create_outlier(widget_outlier, widget_offset)\n",
    ")\n",
    "print(\n",
    "    f\"The slope from linear regression is:         {slope_outlier_fit:1.2f} \\n\"\n",
    "    f\"and the intercept from linear regression is: {intercept_outlier_fit:1.2f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12545cf60>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 5))\n",
    "plot_data_with_outlier(widget_outlier, widget_offset, ax)\n",
    "add_line(\n",
    "    x, slope_outlier_fit, intercept_outlier_fit, ax, \n",
    "    label=f\"linear regression\"\n",
    ")\n",
    "add_line(\n",
    "    x, widget_slope, widget_intercept, ax, \n",
    "    label=f\"eyeball norm\"\n",
    ")\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
